The Moral Ties That Bind . . . Even to Out-Groups: The Interactive Effect of Moral Identity and the Binding Moral Foundations
Bibliographic record
Abstract
Throughout history, principles such as obedience, loyalty, and purity have been instrumental in binding people together and helping them thrive as groups, tribes, and nations. However, these same principles have also led to in-group favoritism, war, and even genocide. Does adhering to the binding moral foundations that underlie such principles unavoidably lead to the derogation of out-group members? We demonstrated that for people with a strong moral identity, the answer is "no," because they are more likely than those with a weak moral identity to extend moral concern to people belonging to a perceived out-group. Across three studies, strongly endorsing the binding moral foundations indeed predicted support for the torture of out-group members (Studies 1a and 1b) and withholding of necessary help from out-group members (Study 2), but this relationship was attenuated among participants who also had a strong moral identity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".